# Efficient learning of large sets of locally optimal classification rules

arXiv: https://arxiv.org/abs/2301.09936 · HTML: https://mlsanity.com/paper/2301.09936/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 70 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Local Rule-Based Explanations of Black Box Decision Systems](https://mlsanity.com/paper/1805.10820/) (2018)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Boolean Decision Rules via Column Generation](https://mlsanity.com/paper/1805.09901/) (2018)
- [Explainable AI for Trees: From Local Explanations to Global Understanding](https://mlsanity.com/paper/1905.04610/) (2019)
- [Scalable Bayesian Rule Lists](https://mlsanity.com/paper/1602.08610/) (2016)
- [Falling Rule Lists](https://mlsanity.com/paper/1411.5899/) (2014)
- [GLocalX -- From Local to Global Explanations of Black Box AI Models](https://mlsanity.com/paper/2101.07685/) (2021)
- [Learning Gradient Boosted Multi-label Classification Rules](https://mlsanity.com/paper/2006.13346/) (2020)
- [Online Decorrelation of Humidity and Temperature in Chemical Sensors for Continuous Monitoring](https://mlsanity.com/paper/1608.01719/) (2016)
- [Better Short than Greedy: Interpretable Models through Optimal Rule Boosting](https://mlsanity.com/paper/2101.08380/) (2021)

## Built on by (most cited first, 7 of 7)

- [Finding Interpretable Class-Specific Patterns through Efficient Neural Search](https://mlsanity.com/paper/2312.04311/) (2023)
- [Scalable Rule Lists Learning with Sampling](https://mlsanity.com/paper/2406.12803/) (2024)
- [Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance](https://mlsanity.com/paper/2407.14543/) (2024)
- [AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking](https://mlsanity.com/paper/2401.11250/) (2024)
- [Personalized Interpretable Classification](https://mlsanity.com/paper/2302.02528/) (2023)
- [VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails](https://mlsanity.com/paper/2610.12813/) (2026)
- [Optimizing Sparse Boolean Rule Networks using Mixed Integer Linear Programming](https://mlsanity.com/paper/2610.20170/) (2026)

## Abstract (arXiv)

Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples they cover. Instead, we propose an efficient algorithm that aims at finding the best rule covering each training example in a greedy optimization consisting of one specialization and one generalization loop. These locally optimal rules are collected and then filtered for a final rule set, which is much larger than the sets learned by conventional rule learning algorithms. A new example is classified by selecting the best among the rules that cover this example. In our experiments on small to very large datasets, the approach's average classification accuracy is higher than that of state-of-the-art rule learning algorithms. Moreover, the algorithm is highly efficient and can inherently be processed in parallel without affecting the learned rule set and so the classification accuracy. We thus believe that it closes an important gap for large-scale classification rule induction.